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An Intelligent Model to Asses Organizational Maturity for Implementation <strong>of</strong> Knowledge Management Kamaladdin Rahmani Youshanloui 1 , Farhad Daneshgar 2 , Nima Sarabi 3 , Hossein Rahmany Youshanlouei 4 and Mohammad Mirkazemi Mood 3 1 Department <strong>of</strong> Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran 2 University <strong>of</strong> Technology Sydney, Sydney, Australia 3 University <strong>of</strong> Tehran, Tehran, Iran 4 Young Researchers Club, Salmas Branch, Islamic Azad University Salmas , Iran & University <strong>of</strong> Tehran, Tehran, Iran kamaladin.rahmani@gmail.com rahmani82@gmail.com f.daneshgar@unsw.edu.au nimasarabi@yahoo.com mohamad_mirkazemi@yahoo.com Abstract: Successful knowledge management implementation requires identification <strong>of</strong> <strong>the</strong> key success factors as well as <strong>the</strong> relationship between <strong>the</strong>se factors. The key success factors <strong>of</strong> KM can be classified by characteristics, situations and variables. This paper aims to develop intelligent models using fuzzy tools for determination <strong>of</strong> <strong>the</strong> relationships between different key success factors in knowledge management implementation. To develop <strong>the</strong> proposed intelligent model, <strong>the</strong> current literature was reviewed in order to identify existing factors. The Fuzzy Cognitive Maps (FCM) method was <strong>the</strong>n utilized to specify <strong>the</strong> cause and effect relationships between <strong>the</strong>se factors. Under <strong>the</strong> FCM method, individuals’ experience is integrated with existing knowledge, and based on that, <strong>the</strong> cause and effect relationships between <strong>the</strong> factors are identified. We argue that our proposed model can be used for exploring ways for enhanced implementation <strong>of</strong> <strong>the</strong> KM systems.The methodology developed uses four matrices to represent <strong>the</strong> results that <strong>the</strong> methodology provides in each one <strong>of</strong> its stages. These are Initial Matrix <strong>of</strong> Factors (IMF), Fuzzified Matrix <strong>of</strong> Factors (FZMF), Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Factors (SRMF) and Final Matrix <strong>of</strong> Factors (FMF). The results <strong>of</strong> this approach can help to identify <strong>the</strong> most important factors to more attention and thus investment <strong>of</strong> <strong>the</strong> organization to enhance implementation <strong>of</strong> <strong>the</strong> KM systems. Keywords: knowledge management, critical success factors, fuzzy cognitive maps 1. Introduction Lack <strong>of</strong> proper mechanisms for <strong>the</strong> implementation <strong>of</strong> knowledge management (KM) has made <strong>the</strong> managers to consider this type <strong>of</strong> investment as only an additional cost. Thus, organizations should provide <strong>the</strong>ir members with environments in which <strong>the</strong>y can share, transfer and compare <strong>the</strong>ir knowledge. They should also educate <strong>the</strong>ir members to signify <strong>the</strong>ir interactions with one ano<strong>the</strong>r and also try to prepare <strong>the</strong> basic infrastructures and identify <strong>the</strong> underlying concepts <strong>of</strong> <strong>the</strong> process <strong>of</strong> KM implementation. By identifying <strong>the</strong>se factors and determining <strong>the</strong> type and amount <strong>of</strong> <strong>the</strong> influence that <strong>the</strong>se factors exert on each o<strong>the</strong>r, <strong>the</strong> organization can focus on <strong>the</strong> most important <strong>of</strong> <strong>the</strong>se factors and increase <strong>the</strong> chances <strong>of</strong> successful implementation <strong>of</strong> KM projects. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> amount <strong>of</strong> <strong>the</strong> influence <strong>of</strong> <strong>the</strong>se factors is almost always stated in imprecise and qualitative terms, knowing that <strong>the</strong> conversion <strong>of</strong> qualitative values to quantitative ones is a complicated process (Xirogiannis 2007). One <strong>of</strong> <strong>the</strong> ways we can use to convert <strong>the</strong>se qualitative values into quantitative ones is <strong>the</strong> Fuzzy Logic. For this purpose, this paper will use <strong>the</strong> FCM method. FCM is a directed graphical chart whose aim is to display <strong>the</strong> cause and effect relationships among factors in a way that <strong>the</strong> relationship between each pair <strong>of</strong> numerical models is defined in <strong>the</strong> range [1, -1] (Kandasamy and Florentin 2003). 2. Literature review 2.1 Knowledge Management Since KM has been investigated and studied through different approaches and many definitions <strong>of</strong> it have been presented, it's difficult to find a definition about which <strong>the</strong>re will be a global consensus. Davenport believes that "knowledge management is an effort to discover latent knowledge in <strong>the</strong> minds <strong>of</strong> people and turn this hidden treasure to organizational assets so that a wide range <strong>of</strong> people, 806
Kamaladdin Rahmani Youshanloui et al who are involved in <strong>the</strong> decision-making <strong>of</strong> <strong>the</strong> organization, have access to this treasured asset and are able to use it (Davenport and Prusak 1998). According to Jung et al. (2003), KM is an integrated systematic approach to identify, manage and share all <strong>the</strong> informational assets <strong>of</strong> organizations which include databases, documents, policies and procedures. KM is also considered as a goal-oriented and systematic approach for <strong>the</strong> organizations to guide and control all <strong>of</strong> <strong>the</strong>ir palpable and non-palpable resources. They partake <strong>of</strong> <strong>the</strong>ir existing knowledge as well as outside sources <strong>of</strong> knowledge to create New Knowledge, Value, creativity and progress (Yang 2004). Drucker believes <strong>the</strong> "secret <strong>of</strong> <strong>the</strong> success <strong>of</strong> organizations in <strong>the</strong> 21 st century is <strong>the</strong> proper implementation <strong>of</strong> KM." So it is necessary for <strong>the</strong> third millennium organizations to implement KM and planning agencies must take action to implement it. The success <strong>of</strong> organizations depends highly on how <strong>the</strong>y can effectively collect, store and retrieve different levels <strong>of</strong> knowledge between <strong>the</strong> personnel (Mumford and Licuanan 2004). 2.2 Key Success Factors Enabling factors have <strong>the</strong> power to guide KM in <strong>the</strong> organization (Allameh et. al. 2011). In <strong>the</strong> Literature <strong>of</strong> key success factors, many definitions <strong>of</strong> <strong>the</strong>m are presented. One <strong>of</strong> <strong>the</strong> most important definitions is Rockart's. Key success factors, according to him, are: a limited number <strong>of</strong> areas <strong>of</strong> activity that will result in a successful competitive performance (Rockart 1979). Bruno and Leidecker, in ano<strong>the</strong>r definition, state that <strong>the</strong> key success factors include: characteristics, conditions or variables that, if managed properly, can have a significant effect on <strong>the</strong> success <strong>of</strong> <strong>the</strong> competitive position <strong>of</strong> organizations (Bruno and Leidecker 1984). Pinto and Slevin, instead, believe that key success factors are those that significantly improve <strong>the</strong> chances <strong>of</strong> successful project implementation (Pinto and Slevin 1987). Skyrme and Amidon (1997) have identified seven key factors regarding <strong>the</strong> implementation <strong>of</strong> KM that include: Strong business requirements, architecture and landscape, leadership, culture, continuous learning, technology infrastructure and organizational knowledge processes. Ano<strong>the</strong>r study to identify key success factors was conducted by Holsapple and Joshi (2000) introduced three main categories <strong>of</strong> factors, namely, Managerial, Resource-related, and environmental and believed that <strong>the</strong> key factors will fall into each <strong>of</strong> <strong>the</strong>se categories. Managerial factors include <strong>the</strong> following four: Coordination, control, leadership and measurements. Resource-related factors include knowledge, people, financial and non-financial resources; and finally, environmental factors include competition, markets, time emergencies, as well as economic and governmental circumstances. Davenport et al (1998) conducted an exploratory study in thirty-one knowledge management projects in twenty-four companies. The purpose <strong>of</strong> this extensive study was to determine <strong>the</strong> key factors associated with <strong>the</strong> implementation <strong>of</strong> KM. Among <strong>the</strong> 18 successful projects, seven key success factors were identified which included: <strong>the</strong> value <strong>of</strong> <strong>the</strong> industry, common goal and language, flexible and standard knowledge structure, multiple channels for knowledge transfer, culture, organizational and technical infrastructure, motivational measures and <strong>the</strong> support <strong>of</strong> <strong>the</strong> senior management. Chourides et al (2003) identified different key success factors, in <strong>the</strong> realm <strong>of</strong> <strong>the</strong> organizational duties, for <strong>the</strong> successful implementation <strong>of</strong> KM. These factors included: strategy, human resources management, information technology, marketing and quality. In a study by Hung et al (2005) regarding <strong>the</strong> key factors <strong>of</strong> success in applying knowledge management system for <strong>the</strong> pharmaceutical industry, seven factors have been identified which include: Strategy <strong>of</strong> Modeling and effective knowledge structure, organizational culture, infrastructure <strong>of</strong> information systems, engaging and educating people, leadership and strong commitment <strong>of</strong> senior management, learning environment, and <strong>the</strong> control and assessment <strong>of</strong> resources. Yeh et al (2006) suggested that enabler agents <strong>of</strong> KM in organizations lead to developing knowledge as well as stimulating Knowledge Creation within <strong>the</strong> organization. They also result in its sharing, and protection which causes <strong>the</strong> improvement and effectiveness <strong>of</strong> KM activities. They, according to <strong>the</strong>ir reviewing <strong>of</strong> <strong>the</strong> literature <strong>of</strong> <strong>the</strong> past and also <strong>the</strong>ir conducted researches, introduced "corporate culture", "People", "information technology "and "Strategy and Leadership" as empowering agents <strong>of</strong> KM. The support <strong>of</strong> Senior Manager is required for <strong>the</strong> most important part, i.e. "strategy and leadership." 807
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Title Author(s) Picture of<
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Inocencia Mª Martínez-León and I
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5.2 Measures Inocencia Mª Martíne
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Maurizio Massaro and Roland Bardy p
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Florian Matthes an
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Florian Matthes an
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Germany - Towards a Knowledge-Based
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Kai Mertins, Sven Wuscher and Marku
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Sandra Moffett, Ro
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References Sandra Mof</stro
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Syed Moneeb Ali, Mark Woodman and A
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5. Evaluation Syed Moneeb Ali, Mark
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Mohammad Mirkazemi Mood, Farhad Dan
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RI = w k j Mohammad Mirkazemi Mood,
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References Mohammad Mirkazemi Mood,
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Knowledge Mapping Based on EFQM Exc
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Mahmoud Moradi, Mohammad Rahim Rama
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The University Institution’s Impr
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Oliver Moravcik, Dagmar Caganova an
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Figure 9: Researchers at th
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Martina Mullally et al A self-learn
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Martina Mullally et al Choo, C. W (
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Fattah Nazem Organizational level
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3. Method of <stro
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Fattah Nazem strengthe</str
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Fattah Nazem Miller, R. (1996). Lea
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Fattah Nazem 1991). Intellectual ca
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Fattah Nazem Table 1: Rotated Compo
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Fattah Nazem Intellectual capital i
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The Knowledge Clinic: Concepts, Met
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Gaby Neumann and Eduardo Tomé capi
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Gaby Neumann and Eduardo Tomé impr
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Gaby Neumann and Eduardo Tomé inst
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A Tenth Anniversary Assessment <str
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Company discussed in Working Knowle
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Gary R Oliver The difficulty with u
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Gary R Oliver accounting precedent
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Gary R Oliver Grant, R. M. (1996).
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Kaspars Osis and Janis Grundspenkis
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Taha Osman, Dhavalkumar Thakker and
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Getting Ready for Knowledge Managem
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Paul Parboteeah, Thomas Jackson and
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Figure 1 Information Management Str
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Knowledge Transfer, Knowledge Shari
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2.2 KBs Dan Paulin and Kaj Suneson
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A Ranking Method for Identification
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Inès Saad criteria are decomposed
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Inès Saad K3 (Crucial knowledge)
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Josune Sáenz, Nekane Aramburu and
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Social Media Mindset and Knowledge
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Risto Säntti social media mindset
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Risto Säntti 4. The social media m
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social media mindsets. Risto Säntt
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Dan Săvescu, Mihaela-Georgia Sima
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2.1 Web analytics evolution Alexand
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Alexander Schneider and Alexander S
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Camilo Augusto Sequeira and Eloi Fe
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9. Conclusions Camilo Augusto Seque
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Elena Shakina and Anna Bykova P. Ra
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Elena Shakina and Anna Bykova Accor
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Elena Shakina and Anna Bykova Figur
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Table 1: The sample descriptive sta
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Elena Shakina and Anna Bykova Table
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Elena Shakina and Anna Bykova It sh
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The Major Challenges of</st
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Mehdi Shami Zanjani, Hamid Rahimian
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Mehdi Shami Zanjani, Hamid Rahimian
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Evangelia Siachou, Sof</str
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Relevance of Intel
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Marta Christina Suciu et al Source:
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Marta Christina Suciu et al Apart f
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Marta Christina Suciu et al public
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Managing Intellectual Capital in SM
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Marta Christina Suciu et al We cons
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Marta Christina Suciu et al aim is
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Marta Christina Suciu et al require
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Universities as Knowledge Creation
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Marta Christina Suciu et al Luintel
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Marta Christina Suciu et al holds a
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Marta Christina Suciu et al propose
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Kaj Suneson and Ilona Heldal Many r
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Kaj Suneson and Ilona Heldal recogn
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Kaj Suneson and Ilona Heldal too ex
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Kaj Suneson and Ilona Heldal In thi
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Infrastructure of
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Natalia Tikhomirova et al global c
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Natalia Tikhomirova et al developm
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Measurement of cus
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Kamila Tislerova and customers, ra<
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Kamila Tislerova as pretty poor (45
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Multi Perspective Framework to Impr
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Choon-Bae Yoo, Igor Hawryszkiewycz
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6.4.1 Content Analysis Choon-Bae Yo
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2. Research objective John T Young
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Figure 2: Exploring and expressing
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John T Young municipal services. Th
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4.1.4 The Customers C John T Young
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6.3 Procedural changes John T Young
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8. The CATWOE Components 8.1 Transf
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John T Young orders could be airfre
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Alexander K. Kofin
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4 : Mobilization of</strong
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Nadine Amende Figure 1 shows an exa
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Figure 3: Classification of
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Nadine Amende such as “What are y
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Nadine Amende Davis, F. D. (1986) A
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Evgeny Blagov internal and external
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Evgeny Blagov oriented on t
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References Evgeny Blagov Arrow, K.J
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Roman Kislov This research looks at
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Roman Kislov role boundary blurring
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Roman Kislov The following methods
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Roman Kislov Lave, J. and Wenger, E
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Tony K. M. Lo and Patrick S.W. Fong
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Type P=A=L P=A, L P, A=L P=L, A P,
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6. Case analysis Tony K. M. Lo and
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Tony K. M. Lo and Patrick S.W. Fong
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KM as a Solution for the</s
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Thi Hai Hang Nguyen et al competiti
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Thi Hai Hang Nguyen et al The varia
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Thi Hai Hang Nguyen et al The resul
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Knowledge Base Development
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2. Literature review 2.1 AC: Theore
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2.3 The inter-relations between SC
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Omid Omidvar Jansen, J. J. P., Van
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Sakura Shimada and that of<
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Sakura Shimada Figure 1: Strategic
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Sakura Shimada broader repercussion
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Sakura Shimada Excousseau, J-L. (20
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Simon Woll The research topic can b
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Simon Woll UI research projects are
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1090
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Chris Blodgett leadership journey t
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Figure 2: Needleman’s Knowledge P
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Chris Blodgett Analysts turn inform
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Figure 8: Cynefin Framework Chris B
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Chris Blodgett IKM Level 1: Inform
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7. Conclusion Chris Blodgett Given
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Stefanie Dannemann However, what wo
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Stefanie Dannemann In modern econom
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2. Research design and methodology
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3.4 KM Applications in IDSC Mohamed
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Mohamed Farid 3.5 Problems with <st
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Mohamed Farid “Enterprise collabo
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Mohamed Farid The design of
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Mohamed Farid For the</stro
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Susan McIntyre and Kate Kaminska re
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Susan McIntyre and Kate Kaminska C
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Susan McIntyre and Kate Kaminska Th
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Susan McIntyre and Kate Kaminska il
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1128
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Óscar Arias Londoño mention that
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5.4 Tacit Knowledge Óscar Arias Lo
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Proposal For A 2.0 Knowledge Manage
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Lillyana María Giraldo Marín et a
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Similarity and Accuracy of<
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Nina Groß et al reporting and cont
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Ravinder Singh Kahlon and Man-Chie
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Figure 3: Non-IT and IT Kiviat Diag
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Appendix B Ravinder Singh Kahlon an
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Pushkal Pandey, Sandra Mof<
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Pushkal Pandey, Sandra Mof<
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José Vásquez Paniagua According t
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José Vásquez Paniagua the
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